1. Jan 2005Statistical MT1 CSA4050: Advanced Techniques in NLP Machine Translation III Statistical MT

2. Jan 2005Statistical MT2 Statistical Translation Robust Domain independent Extensible Does not require language specialists Uses noisy channel model of translation

3. Jan 2005Statistical MT3 Noisy Channel Model Sentence Translation (Brown et. al. 1990) source sentence target sentence sentence

4. Jan 2005Statistical MT4 The Problem of Translation Given a sentence T of the target language, seek the sentence S from which a translator produced T, i.e. find S that maximises P(S|T) By Bayes' theorem P(S|T) = P(S) x P(T|S) P(T) whose denominator is independent of S. Hence it suffices to maximise P(S) x P(T|S)

5. Jan 2005Statistical MT5 A Statistical MT System Source Language Model Translation Model P(S) * P(T|S) = P(S|T) ST Decoder TS

6. Jan 2005Statistical MT6 The Three Components of a Statistical MT model 1.Method for computing language model probabilities (P(S)) 2.Method for computing translation probabilities (P(S|T)) 3.Method for searching amongst source sentences for one that maximises P(S) * P(T|S)

7. Jan 2005Statistical MT7 Probabilistic Language Models General P(s1s2...sn) = P(s1)*P(s2|s1)...*P(sn|s1...s(n-1)) Trigram P(s1s2...sn) = P(s1)*P(s2|s1)*P(s3|s1,s2)...*P(sn|s(n-1)s(n-2)) Bigram P(s1s2...sn) = P(s1)*P(s2|s1)...*P(sn|s(n-1))

8. Jan 2005Statistical MT8 A Simple Alignment Based Translation Model Assumption: target sentence is generated from the source sentence word- by-word S: John loves Mary T: Jean aime Marie

9. Jan 2005Statistical MT9 Sentence Translation Probability According to this model, the translation probability of the sentence is just the product of the translation probabilities of the words. P(T|S) = P(Jean aime Marie|John loves Mary) = P(Jean|John) * P(aime|loves) * P(Marie|Mary)

10. Jan 2005Statistical MT10 More Realistic Example The proposal will not now be implemented Les propositions ne seront pas mises en application maintenant

11. Jan 2005Statistical MT11 Some Further Parameters Word Translation Probability: P(t|s) Fertility: the number of words in the target that are paired with each source word: (0 – N) Distortion: the difference in sentence position between the source word and the target word: P(i|j,l)

12. Jan 2005Statistical MT12 Searching Maintain list of hypotheses. Initial hypothesis: (Jean aime Marie | *) Search proceeds interatively. At each iteration we extend most promising hypotheses with additional words Jean aime Marie | John(1) * Jean aime Marie | * loves(2) * Jean aime Marie | * Mary(3) * Jean aime Marie | Jean(1) *

13. Jan 2005Statistical MT13 Parameter Estimation In general - large quantities of data For language model, we need only source language text. For translation model, we need pairs of sentences that are translations of each other. Use EM Algorithm (Baum 1972) to optimize model parameters.

14. Jan 2005Statistical MT14 Experiment 1 (Brown et. al. 1990) Hansard. 40,000 pairs of sentences = approx. 800,000 words in each language. Considered 9,000 most common words in each language. Assumptions (initial parameter values) –each of the 9000 target words equally likely as translations of each of the source words. –each of the fertilities from 0 to 25 equally likely for each of the 9000 source words –each target position equally likely given each source position and target length

15. Jan 2005Statistical MT15 English: the FrenchProbability le.610 la.178 l’.083 les.023 ce.013 il.012 de.009 à.007 que.007 FertilityProbability 1.871 0.124 2.004

16. Jan 2005Statistical MT16 English: not FrenchProbability pas.469 ne.460 non.024 pas du tout.003 faux.003 plus.002 ce.002 que.002 jamais.002 FertilityProbability 2.758 0.133 1.106

17. Jan 2005Statistical MT17 English: hear FrenchProbability bravo.992 entendre.005 entendu.002 entends.001 FertilityProbability 0.584 1.416

18. Jan 2005Statistical MT18 Bajada 2003/4 400 sentence pairs from Malta/EU accession treaty Three different types of alignment –Paragraph (precision 97% recall 97%) –Sentence (precision 91% recall 95%) –Word: 2 translation models Model 1: distortion independent Model 2: distortion dependent

19. Jan 2005Statistical MT19 Bajada 2003/4 Model 1Model 2 word pairs present244 word pairs identified145 correct5877 incorrect8768 precision40%53% recall24%32%

20. Jan 2005Statistical MT20 Experiment 2 Perform translation using 1000 most frequent words in the English corpus. 1,700 most frequently used French words in translations of sentences completely covered by 1000 word English vocabulary. 117,000 pairs of sentences completely covered by both vocabularies. Parameters of English language model from 570,000 sentences in English part.

21. Jan 2005Statistical MT21 Experiment 2 contd 73 French sentences tested from elsewhere in corpus. Results were classified as –Exact – same as actual translation –Alternate – same meaning –Different – legitimate translation but different meaning –Wrong – could not be intepreted as a translation –Ungrammatical – grammatically deficient Corrections to the last three categories were made and keystrokes were counted

22. Jan 2005Statistical MT22 Results Category# sentencespercent Exact45 Alternate1825 Different1318 Wrong1115 Ungrammatical2737 Total73

23. Jan 2005Statistical MT23 Results - Discussion According to Brown et. al., system performed successfully 48% of the time (first three categories). 776 keystrokes needed to repair 1916 keystrokes to generate all 73 translations from scratch. According to authors, system therefore reduces work by 60%.

24. Jan 2005Statistical MT24 Bibliography Statistical MT Brown et. al., A Statistical Approach to MT, Computational Linguistics 16.2, 1990 pp79-85 (search “ACL Anthology”)